Shayan Intekhab (シャヤン)

Co-Founder

Tokyo, Japan4 yrs 3 mos experience

Key Highlights

Developed comprehensive healthcare applications using Flutter.
Collaborated internationally on NLP projects.
Co-founded a tech startup focused on innovative solutions.

Stackforce AI infers this person is a Software Engineer with a focus on Mobile and AI applications in the Healthcare sector.

Contact

Skills

Core Skills

Mobile Application DevelopmentFlutterFirebaseMachine LearningNatural Language Processing (nlp)C++SolidworksAndroid DevelopmentPython (programming Language)

Other Skills

AlgorithmsAutodesk InventorBitriseC (Programming Language)Cascading Style Sheets (CSS)Computer VisionDartData AnalysisData AnalyticsData StructuresDatadogDeep LearningEvent ManagementGraph TheoryHTML

About

Learner (学習者)

Experience

4 yrs 3 mos

Total Experience

9 mos

Average Tenure

9 mos

Current Experience

Fast retailing

Software Developer

Sep 2025 – Present · 9 mos · Tokyo, Japan · On-site

FlutterMobile Application DevelopmentQueue-itBitriseDatadog

Samaritan ai

SDE Intern

May 2024 – Aug 2024 · 3 mos · New Delhi, Delhi, India · On-site

Developed a series of full-fledged Flutter applications from scratch, including a comprehensive hospital management system.
Integrated robust functionalities such as managing and displaying data from a urology device with critical patient information.
Included features like graphical representations of data, API integration, Bluetooth connectivity, and custom-built chat function.
Implemented authentication and cross-registration, and developed and integrated computer vision models for device scanning.
Built a graph database using shared_preferences and sqflite package and utilized Firebase for backend services.

Deep LearningPython (Programming Language)FlutterMachine LearningNeo4j

The university of auckland

Summer Microintern

Jul 2023 – Aug 2023 · 1 mo · Auckland, New Zealand · Remote

Collaboration: Project for a New Zealand-based company
Collaborated internationally to build a system that distinguishes between place names and other entities in text using geotagging.
Used Transformer models to improve accuracy and contextual understanding in entity recognition.
Enhanced the overall efficiency of the geotagging process for large-scale text analysis.

Natural Language Processing (NLP)